Pedometer Accuracy in Nursing Home and Community-Dwelling Older Adults
Bibliographic record
Abstract
PURPOSE: The accuracy of pedometers has not been thoroughly tested with older adult populations. The purpose of the present study was to examine the effects of walking speed and gait disorders on the accuracy of Yamax pedometers with nursing home residents (NH) relative to older adults living in the community. METHODS: Pedometer accuracy was evaluated against observed steps taken during a self-paced walking test (slow, normal, and fast speeds) in 26 NH residents and 28 seniors' recreation center members (SC). Devices were attached to clothing at the waist. Walking speed was ascertained from the timed walk and a gait assessment was conducted. Percent error was calculated as ([pedometer steps - observed steps]/observed steps) x 100. RESULTS: The walking speeds of both samples increased across self-selected paces (P < 0.0001). The community-dwelling older adults walked significantly faster (P < 0.0001) in all trials and had significantly higher (P < 0.0001) gait assessment scores (indicating fewer gait problems). Gait scores were positively associated with walking speed and pedometer percent error. Pedometers significantly underestimated NH residents' observed steps taken by 74% (slow), 55% (normal), and 46% (fast) paces (P < 0.0001). In the SC sample, the instruments failed to detect 25%, 13%, and 7% of actual steps taken, respectively (P < 0.0001). The magnitude of the error was greater for NH versus SC older adults (P < 0.0001) across all trials. CONCLUSIONS: Slow walking speed and gait disorders hamper the utility of pedometers for physical activity measurement in frail seniors, such as NH residents, when worn at the usual attachment site. Pedometers, however, can be confidently used with ostensibly healthy older adult populations for both assessment and motivation purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".